Web Analytics: Frequently Asked Questions And Direct Answers

After 416,350 words in posts and 845,128 words in comments on this blog, thus far, there is always more to explore, illuminate and share. Hence every once in a while I flip the tables and ask you for challenges you are facing.

It is a great way to stay connected to what's most important to you (and keep the blog and its content relevant!). This past Monday I asked for your questions and you were kind enough to share some awesome questions. Thank you.

I'm going to try and answer all of them here. But since the questions alone made up more than 1,000 words, I am going to try and keep the answers as pithy as I can while trying to give you an answer to chew on.

To keep things a bit organized the questions are organized into four buckets. They are: the tactical "How do I?", the strategic "How can we?", the abstract "How come it's not that way?" and finally, the surprising "How can I possibly answer that?"

It is going to be fun. Let's go!

Nose to the grind "How do I" questions:

David Walizer: How do you sell the value of web analytics to a skeptical client in 30 seconds or less?

By saying this:

"Web Analytics will help add $2 million worth of additional Economic Value by recommending specific ways to delight our customers and improve the way we sell"

You can say that in 10 seconds.

The challenge is that you should have done enough work upfront to know what's important to the business, got a rough sense for things you can fix right away and their value, and then done some back of the napkin calculations about the Economic Value your fixes will add. That takes a few days of pain. But there is no alternative.

If you are in a company this is easier to get done as you have access to people and at least some data (even if the site is not tagged). If you are a consultant then identifying opportunities is a smidgen harder, but you can use your experience with other clients to quantify value.

We all look for shortcuts, in this case some magical words from an enchanted fairy who lives in the mythical city of Oz.

Nilaye Thakrar: What is the best way to attribute an offline sale to an online assist?

By doing multichannel analytics!

Your problem is the primary key. So use unique phone numbers (specific to campaigns if you want granular details)… leverage unique coupon / campaign / offer codes… get good at geographic targeting… become a God of controlled experiments.

Paid Search. For AdWords accounts that are linked to Google Analytics in very rare cases it happens that "click" data is available from AdWords for certain keywords but there is no Visit data available in Google Analytics. Perhaps because the tracking code did not fire off or, more likely, there was no tracking code on the landing page. Then you'll see some zeros in the visits column. One way to verify is to click on Traffic Sources > Campaigns > Clicks tab to see the non-zero impressions from AdWords.

Organic Search. Let's use this example.. You come to the blog on the search phrase "Avinash is awesome," you land on a irrelevant page on this blog, you hit the back button and go back to Bing, you try a different search phrase "Avinash is not awesome," you land on the right page, you love it, you read lots of post, you leave. So how does GA decide which keyword to assign that visit to? Should each keyword show one Visit? That would not be right. Should it be the first search phrase you came on? The second one?

GA will show you both, but put a zero for the second. Essentially it is assigning "credit" (attributing) the visit to the first keyword ("Avinash is awesome"), and a zero in Visit for the second keyword ("Avinash is not awesome"). But each keyword gets "credit" for other metrics. So if you had seen three pages on the keyword "Avinash is awesome" then it will show one visit but it will show three page views. And if you came back again, in less than 29 mins on the keyword "Avinash is not awesome" and saw ten pages and converted then that kw will show zero visit, 10 pages and one conversion.

IMPORTANT: This type of behavior is rare so you should not see 0 visits often.

[ Update: Technical explanation via Cristina Chetroi: What happens is that when you access the site for the second keyword, the __utmz cookie gets updated with the new keyword, but, as the visit had already started when you accessed the website via the first keyword – the 1 visits gets attributed to that initial one – the first one. This way GA will show you both keywords while the aggregate for visits will not be skewed – as technically it is just one session.]

There are many, many paid tools in the market (like HitWise and Compete and Netsuus). Above are just the free ones whose datasets are is large enough to provide good data. They are all Google tools

Benjamin Rodde: I'd like to predict our Daily/Weekly/Monthly churn using the #GooglePredictionsAPI with unique visitors from #GoogleAnalytics.

The best option is to hire a statistician with experience in data modeling and forecasting. In case she/he does not have programming experience, hook them up with a technical person who has modest technical skills.

Two other quick things…

Churn is a term most closely associated with customers you have acquired (and then failed to retain) and not so much to "fly by night" Visitors on your site. The latter, except in rare cases, is hard to do predictive analytics on unless you are a stagnant business.

If you want to do churn analysis, as defined above, then you don't want GA as much as you want your back-end ERP / "Orders" database that has your customer history, and just a couple of acquisition signals (campaign and source come to mind). You can and should do that now (along with customer lifetime value type valuable computations).

Brian Krick: Best way to measure and communicate "available demand" from available channels (social, search, display) for forecast modeling

Please see the advice above.

Additionally, it is exceptionally difficult to measure available demand because 1. the web is so insanely fluid, 2. there are so many variables that drive demand (not just online but also offline and events etc.), and finally 3. because the data is so very not available.

(These are also reasons why click attribution to multiple campaign prior to conversion is such a thankless exercise. Implicit in that is the assumption of infinite demand, among other problems.)

I have personally had a lot of success using Controlled Experimentation techniques, such as, say, Media Mix Modeling, to understand both current available demand and also segment conversion effectiveness. And this has to be a continuous approach and not discreet. If you have Web Analytics 2.0 please refer to the controlled experimentation section, page 205, in the book for more.

Peter – IJsbeer: How to gain insights to improve competitive keywords rankings of websites? And to visualize it in a report..

Do regression analysis that factors in all the available variables (and be really thorough here) to isolate the relationship, as Wikipedia says, "between a dependent variable and one or more independent variable."

Chris Rinaldi: What's the most accurate way to discern a competitor's web traffic (at least 'visits')?

The highest boost in accuracy will come from educating yourself on precisely how data is collected by competitive intelligence tools. You can then understand how to choose the right tool – depending on the location of the competitors (U.S. or non-U.S.), the rough size of the traffic they might get (panel based systems suck at any analysis of sites that might get less than one million unique visitors a month), and line this up with your ability to pay (you go from free to really expensive, for example, as you move from Trends for Websites to Compete to HitWise).

You have to be able to recognize when there simply is not enough data, and in our WA context, this happens a lot. There are things like conditional logistic regressions that you can apply to tease out some insights. It is hard.

I have personally learned to switch to "What else can I do?" So for example, if the site has a very small sample, I could switch to a simple one question on-exit survey to get some VOC to get more hints as to what's up. Or maybe just dump the whole data from visitors and switch to expert analysis (conceptfeedback.com type analysis). Or maybe simple usability research. Or, worst of all when the gun is to my head, "best practices."

None of these are perfect but I like multiplicity because it lets my experience triangulate various sources and I can fill in some of the gaps in the sparse data from which I can't find insights with enough confidence.

Strategic "how can we" questions:

Simon: How to "sell" GWO testing (+Analytics) to Managers, very limited time/budget/staffing (btw, we already use Analytics and Adwords, but GWO is proving harder to get prioritised and understood, maybe worry of complexity).

If there is not budget or time or staffing then you might be hitting your head against a brick wall and sometimes recognizing that this is a waste of time and waking away is the most optimal thing to do.

But if there is an opening, then there is one sure way to convince almost anyone (to use analytics or testing or WebTrends or surveys or whatever): Compute the economic value of following your recommendation.

For that you have to know the goals, some goal values, and have some sense for how much improvement you can drive.

Jon Whitehead: Why does the public service have such a massive aversion to measurement? Silos, budgets, internal comms all influence.

There is no tradition of accountability in almost every country when it comes to public service (there are some exceptions like Singapore).

No accountability = very little desire to measure.

There has to be fundamental massive change to a bureaucratic, siloed, politicized institution populated by non-relevant people at the top environment. Else you and I, Jon, will slog in vain, or at best, move in minor increments along a local maxima.

Chaudhry Javed Iqbal: In a non profit organisation, how to convince business managers that their is more to analytics than just unique visitors?

With a WAMM in place I promise you that they will care and want good metrics because they would have contributed to the top part of the WAMM which gets them to put skin in the game.

Webbingyourway: If you had one wish that would force all analysts to do one thing perfectly all the time when analyzing, what would it be and why?

Get really good at measuring Primary Purpose & Task Completion Rate, and then segment that data and analyze.

It is exceedingly rare that we understand all the reasons people come to our site. It is common for our leaders to have a non customer-centric view of the world. Both of these flaws result in our analysis being significantly more narrowly focused than it should be.

Use 4Q, use KissInsights, use something else you like. Get into measuring this regularly. Bring customer-centricity to your analysis, take your understanding of that data and now identify all the Macro and Micro Conversions in your web analytics, measure holistic success and celebrate the massive acceleration of your salary!

Reason enough to do this? : )

Suzy Sandberg: Easily understanding the incrementality of all marketing channels without having to compare order IDs.

I LOVE doing this.

Simple answer: Media Mix Modeling or Marginal Attribution analysis.

Both require patience and love, the former a bit more than that latter.

More details in Chapter 12, pages 366 – 368, in Web Analytics 2.0.

Zyxo: Do you know 1 company that mixed web data and customer data for marketing purposes ? Results ? Thx.

I personally don't know any company that has done this: "Let us build a massive data warehouse of multiple years of clickstream data with all our customer data and look, we have orgasmic insights." 100% of those efforts have lead to failures.

VERY, VERY IMPORTANT: This could just be a blind spot for me. There very well could be hundreds of companies that have done this and are thriving.

I have seen quite a few companies that have taken a selective subset (typically a handful of values related to acquisition dimensions) and put that into their corporate data store and used that to do remarketing, or computing customer lifetime value, or other marketing purposes. The centrality was not web data, it was the corporate multi-channel customer data. The web data happened to be one small part, but an important one.

Diogenes Passos: I got problems on communicate under performing micro conversions [content based websites]. Would love to hear practical advices.

Diogenes I am afraid I don't understand the question.

But if getting people to take action on your recommendations is a challenge then I encourage you to look at the two spreadsheet examples in this post, Barriers To An Effective Web Measurement Strategy, and use that type of a framework, especially the last column with red font which quantifies the impact of non-action.

It always works as a swift, but lovingly-delivered, kick to the rear end.

Sarah E. Bowser: Does web analytics help the internet field overall or hurt the user and privacy rights? Where's the line?

Analysis of data of their website by owners of that site unquestionably helps them and the internet field and the users.

With regards to user privacy. . . I was recently asked about it and our (analyst) behavior and analytics tools. Here's my answer:

As a consumer you should know what the privacy settings are in your browser and use them. For example, I don't allow third party cookies to be set in my browser. If you visit certain sites frequently then glance at their privacy policies and if you think they are untrustworthy use them in a browser where you have JavaScript and cookies and flash etc turned off (one of my browsers is preset to this mode, IE!). Ultimately you are responsible for your own privacy.

As a website owner you should have a very very clear privacy policy that says in as simple terms as possible what you track and instructions on how to opt out. Here's my humble attempt: Occam's Razor Privacy Policy. An informed customer that trusts your website is always better. Oh, and don't be one of the jackhammers who snoops your customer's browser history and does other sub-optimal stuff. It is just not worth it, no matter how cool you think it is.

As a web analytics vendor you should provide choice to the businesses that use your tools. Don't be one of those lame vendors that use third party cookies to "opt out of tracking." Have the courage to develop better opt out options like plugins. If a business's website visitors have choice they are more likely to trust the website they are on, which is great for everyone.

As a web analyst, don't be the aforementioned jackhammer. It is simply not worth it. Even if you can't track 30% of your website traffic, know that you have 50x more data than is available via any other Marketing channel. Collect anonymous data. If you need PII data for analysis (and only 1 in 1,000,000,000,000 do), then your first choice should be to store it outside cloud based analytics tools. And if you store it in cloud based tools, then for the love of all that is beautiful in this universe please disclose it clearly in your website's privacy policy.

Consumers should worry about privacy. They should always have a choice. We (consumers, site owners, vendors & analysts) should ensure that choice exists, and we should work hard to earn the trust of website users.

Hope this helps.

Analytics Ninja: The best way to create a multiple touch attribution model for conversions.

I wish I could give you a pithy answer.

Perhaps you already have a copy of my second book, just jump to Chapter 12.

If you plot the number of doctors in the city and the number of murders in the city you'll notice that the correlation might seem pretty tight. So you, or I, could declare "Reduce murders, eliminate doctors!"

:)

We do that in web analytics every day. Our only salvation is to consistently seek to establish causation.

Tara Dunn: My one question would be, where do you draw the line between using data for answers vs. using your own logic? It seems like I often run into scenarios where the analysis will only take me so far but it still doesn't tell me WHY. So then I have to switch roles, from analyst to scientist, and build hypothesis and test them. I would be interested to hear when you switch from analyst to scientist, and if you think there are other "roles" necessary.

I answered this in a recent comment where Erica asked me: Do you get insight from data or find data from your insight?

Here's my answer:

The answer of course is: Both. This is why *analysis* is so important, and reporting is just something we have to do to survive in this world.

When you are doing analysis sometimes you are in an exploratory mindset, you know the goals of the company and you are exploring trends and patterns in data to find insights. At other times you'll have a hunch or a gut feeling about something or (my favorite) a hypothesis about something, and then you'll don your analyst hat again and you'll analyze data to validate your hunch / gut feel / hypothesis.

So insight from data or data from insight depending on your starting point.

I requested Dorota to define what she means by predictive analytics. She kindly defined it: As using past visitor data (web analytics) to generate actionable insights about future visitor behavior, whether through modeling, etc.

In context of that definition… I am afraid that I don't think we are there yet. We have not solved the problems outlined in the 2007 blog post. Wait. That's not right. Our technology is still the same, the data we collect is the same (with the same fragility), the holes that existed still exist, the environment we have to do PA is not any better (and it does not matter if you have a massively complex data warehouse).

But.

It is important to realize that there is one "predictive analytics" you can and should do if you are a largish ecommerce (outcomes) type website. The behavior of those who actually buy. You have loads of info about them (including PII), you can tie it to their other purchases / contacts (offline), you can try to "predict" repeat purchase rates, attach rates, likelihood of this or that centered on outcomes. This will cover 2% of the traffic, but of course it is all your revenue.

What people will share publicly is changing fast. A certain social media company whose name starts with F and ends with ook :) is pushing boundaries of what data is captured and then shared with partners and how it is used. This is very different from the data we capture today in the web analytics world. In the near future this will, I think, change what analytics vendors consider data. We are entering a world in which we can tie a visit to a person with supreme confidence, a person who is not just a cookie but we know likes to listen to the Jonas Brothers, wears pink underwear, has three iPads and has just bitched about Delta airlines. Just think of what you can do with that.

The challenge is not that we don't have algorithms, the challenge is not that we are not bright… the challenge is the number of and cleanliness of known and unknown variables we can input into the data-set we have. This will change with time.

I hope never. It would be horrible to be "absorbed" by BI as it is today.

My experience in the world of Business Intelligence still surpasses my experience in the world of Web Analytics. I have not only lived there, but done the down and dirty, and have the bruises and some trophies from my time in BI.

BI is massively IT-centric, slow moving, controlled by a centralized team of report writers who fulfill requests based on a painfully prioritized processes on a monthly schedule from data sources that are have the agility of a turtle carrying a thousand ton weight, powering big decisions infrequently.

WA is, for the most part, owned by business teams with data stores in clouds with little corporate IT involvement, the coolness of capturing more fascinating data faster and, and, and ability to analyze the data with the agility of a turtle with no weight, powering small and medium decisions every week.

WA moving into the above-described IT-centric environment would be the kiss of death.

BI does have the benefit of corporate buy in (how else would you pay sixteen million dollars to Oracle each year for your "backend" and five million a year to SAS for the "frontend?"). They have the golden data (Ohh PII I love you so!). They have an established history of proven algorithms and models and mathematical techniques and all those approaches Jim Novo and Kevin Hillstorm keep talking about!

WA's got none of that. Okay okay so we are just a 4-year-old child and BI a 65-year-old.

The old "offline world is dying," its way of doing business is dying. The web is current, digital and its demands of how business should be done are the future. I hope that a new field emerges, let's say called Cutroni Analtyics, that absorbs the discipline and the analytical methods and rigor (and money) of BI and adopts the agility, cloud based non-kill-me-now-rather-than-wait-for-9-years-for-IT-to-implement-something-death-grip way of doing business-centric analysis.

In the end, I personally don't care what its called. I pray to Jesus and Allah and Krishna that it is a hybrid that contains the best of both worlds. That's what we need.

Joe Teixeira: What will it take to finally (FINALLY) "make it" to the big time as an industry?

It will ultimately take becoming central to every company's existence. Perhaps it takes the hybrid I have described in reply to Justin's question above.

But I don't see that happening anytime soon, the worlds of traditional business intelligence and web analytics are populated by people and mental models as different as. well to borrow a popular metaphor. Mars and Venus.

I do not believe we (taking a web centric view for just a moment) are that far off from big time. If every single Analyst throws off the yoke of being a Reporting Squirrel and focuses her/his work on tying every single analysis they do to Net Income then I think we will become BFFs of Sr. Leaders very quickly.

At the very minimum that will give us a really solid shot at being relevant.

Naqaash Pirani: What best practices in web analytics can be applied to social media? Are there any similarities between the two mediums?

Hmmm…

They are same and they are different. They are same in that an obsession, an absolute obsession, with outcomes is mandatory. They are different in that the desirable outcomes have to be rethought (from a website analytics context).

Dominic Parker: What do you think is the best web analytics package out of Google Analytics or Site Catalyst?

Hmm…

It is foolish for anyone to state that one package is better than another one for everyone. If someone from Google Analytics is saying that, they are exhibiting a lack of critical thinking brain neurons. Ditto if someone from Adobe or IBM says that.

We are blessed with a diverse set of tools in the marketplace. Spend some time trying to figure out which one is right for your current needs not what you might need 96 years from now. (See the 3 Questions link immediately above.)

To base your decision on what I think, or an "industry analyst" or your lover is really. what's the word I am looking for. hmmm. let's just say not good.

Timur Khamitov: What is the MAIN value driver of over-all conversions: rankings, rich content, UI, IA etc? Guess what I mean is, in your experience: what is the 1 thing that delivers results above all else.

Hmmm…

The one thing that consistently delivers results is delivering customer delight.

That could mean doing all the things you mention in your question well, or delivering some of them really well and not totally sucking at the rest.

So much stuff for you to read, learn and become awesome. The ball truly is in your hands.

Before we go… I want to thank you for being engaged and for asking such wonderful questions. You force me to think harder, you keep me connected to reality, you help me become a better Practitioner of the art of business analysis. Gracias. Arigato. Merci.

Okak its your turn now.

Which question did you find most astonishing? Which answer did you find surprising? Which answer do you think is completely off the mark? How would you have answered Alex's sparse data question? How about Justin's BI question? Do you agree with my perspective and tough love to Analysts on privacy?

My one caveat to Compete.com like most other competitive intelligence tools is that these tools tend to ONLY get the EXTREMES right for a lot of their reporting. For example, if you're a website with relative moderate traffic or you're a website with a million uniques a day, then competitive intelligence tools will usually be right on the money.

BUT… however if you're somewhere in the middle like a Saks Fifth or a ParezHilton site then chances are a lot of the reports will be fairly inaccurate. My advice is don't let competitive intelligence tools tell you things that your web analytics tool can do – EXAMPLE, most searched KW phrases, top visiting exit pages, etc etc. Instead, rely on competitive intelligence tools to show you trends of how your end user behaves on other websites.

OH and FYI the AGE OLD DEBATE OF GA vs OMNITURE!!!

Omniture will ROCK if implementation is spot on – but just keep in mind that site-catalyst ALONE is very CONVERSION centric. What I mean is that Omniture is very focused on tracking what's happening if XYZ occurs with 123.

GA on the other hand is very USER centric and very intuitive and figuring out "low hanging fruit" information about your audience.

MY ADVICE SINCE GA IS FREE IS TO USE BOTH. Benchmark all the surface level metrics like visitors, (basic) conversions, etc and then find the margin of error between the two. GA is always off by 3%, SC is always off by 5% and then work from there.

I forgot to mention those Google tools as I use them as well, but I did not know about Doubleclick ad planner. I am surprised that Ad Planner isn't mentioned as much (overall in the Analytics realm) as it should be.

Thanks again for the insight. Great post, you should definitely do more like these!

Thanks for answering my question! Your answers are always valuable, even when they are short. ;) I can't help but laugh when I think about my job sometimes – it is very much based in data but there are so few easy definitions.

I've been thinking about it, and I think my strategy goes like this: I start as the scientist, even subconsciously forming a hypothesis, then I become the analyst and discover answers in my data (or not) and maybe a few more hypotheses. Rinse, repeat. Sort of like a chicken/egg story – one cannot exist without the other, and really it doesn't matter who comes first.

Nelson: I am not sure that I completely agree with the Compete comment. For example from a trend perspective Compete's data for this blog (it is a small site) is directionally (beyond the extremes) correct. But you are right that the composition of the traffic, source and other factors should be evaluated.

(This is not a good day… :) I also disagree with your GA – Omniture comment. Your observation is, my apologies, an old cliche that is no longer true.

And I do highly recommend monogamy with web analytics tools. If you have Site Catalyst then move beyond constant implementation and use the darn thing, it is a great tool. If you have GA or WebTrends or CoreMetrics… ditto.

I have yet to meet a company that was getting so many insights from web analytics and was driving so much business action that they were blessed with people & time to implement a new tool, double tag all campaigns, special tag campaigns and events, multi-tag rich media experience on the site, hire staff with double skills and so on and so forth.

Landin: Most of web analytics is so focused on search and email and all that, we are rarely focused on Display and AdPlanner is a tool built for display advertising. Hence you don't hear more about it. And that's too bad because not only can we do better Display advertising with it, there is so much we can use in the normal course of our website analysis job.

Tara: As much as we would like to believe that things are black and white (especially since we live in a hard quant WA world), they really are not. It is very hard to calibrate our thinking to be fluid and move without pain between the different roles in our heads (multiple times a day).

First of all, I am honored that you answered my question. I think one of the public concerns or qualms with Analytics is that they think it infringes on their privacy, and I've gotten into many heated debates over this topic, but mostly with people who do not understand the field itself.

The first argument about it involved my Dad. My Dad is not the most internet savvy person, but he seems to think that if any piece of data is collected about him on the internet than the collector of that data is infringing on his privacy rights.

I think that this point of view is something that is going to plague the field and become an issue. The better we get at our analysis, the 'creepier' we get with our insight. (And by 'creepier' I mean accurate and knowledgeable.)

I completely agree with your answer to my questions, and I appreciate you taking the time to address them.

I only hope that all users of the internet will be made aware of the opportunity for the sophistication of the internet, and it's systems, through Web Analytics.

I've felt sorely disappointed with Compete for the last 3 websites I used them for. For example, when looking at competitive intelligence and trying to see top referrers or top searched KWs, I've always gotten JUNK. In my mind, I think sampled data from user's top box is misleading unless the website has enough niche traffic to trend long term.

All in all, I've just been disappointed with Compete's data – especially for ecommerce. When running Compete through the companies I work for, they always get my data wrong for some reason. I suspect that they might have your data right on the money because your website is so prominent? IDK I'M REALLY GUESSING at this point but I'll give Compete another try.

Looking back at what I said. You are TOTALLY right about sticking with one web analytics solution. But let me curb my statement from the POV of how large corporations are approaching web analytics now – always buying into Omniture when GA will work better for them.

My only problem with sticking with one solution is that I've rarely been to a place that has Site-Catalyst implemented properly. And then in order to do INTUITIVE segmentation, you gotta buy into the whole wheel, e.g. Discover. Also, my issue with buying strictly into Omniture is that without a strong grasp of the tech behind web analytics, it's difficult to generate solid insights with Site-Catalyst alone. Site-Catalyst was very unforgiving when I was pretending to be a web analyst some many years ago. And without really being able to tie "business outcomes" with "what do I need to capture," I always ended up becoming a reporting squirrel much like my hippos. Again, I'm not disagreeing with you, rather I'm trying to highlight the current industry in NYC as I see it now:

* the clients opt in to SC for the real-time when they don't even have the traffic volume for it

* Fortune 50s (that I'll keep anonymous) can't even use Omniture for some of their products because online channels get too much volume. (A side note: the company I'm thinking off actually has a Adobe Tech 24/7 for other products.)

* companies RARELY RARELY get the tagging right because they don't lay out business outcomes (Web Analytics 2.0)

LOL Long comment. Basically, yes Avinash you are right about the GA – Omniture. But if companies already make the mistake of buying into Omniture without assessing the brain power or the business needs, I would implement GA at the same time.

"I have yet to meet a company that was getting so many insights from web analytics and was driving so much business action that they were blessed with people & time to implement a new tool"

I've yet to find a great web analyst that only knew how to leverage one tool. You're living proof no? LOL I kid and joke. Not every GA or Site Catalyst user can be a great web analyst, but a great web insights can come from any tool. I guess what I'm trying to say is that the best web analysts (and the ones I aspired to be like when I was young) are the ones that know the Ajax tagging nuances like the back of his/her hand or can properly fire off Q&A when an eVars isn't firing properly.

Hi Avinash. While I loved this article and every Q&A in it, I think your answer to Simon on selling the value of GWO/testing is missing one important thing: your colleagues have to know what it is!

I find often in the online marketing space that much of the challenge in getting buy-in comes from people understanding exactly what it is I'll be doing. Can anyone relate?

I typically don't even have to get to numbers (but they do help) to gain buy-in for CO testing. Most people are excited to begin simply because testing sets them free to discover the best outcome instead of using their judgement to pick a direction.

Hey! This post was astonishing!
Surprising and happy to see you have chosen my simple question among really great ones by #measure folks!

In advance: you not only answered my question but made me think really some steps forward.

Allow me to be selfish and talk a little more about my question, which was: "I got problems on communicate under performing micro conversions [content based websites]. Would love to hear practical advices."

Hard to detail within 140 characters. I work mostly on content websites, naturally based on ad revenue monetization.
The true approach to measure not only macro but also micro conversions [which I did learn (still learning!) at this blog. Oh yeah] no doubt made a big difference on the products which incorporated it on their analytics efforts. But here and there I faced under performing micro goals.. and I was thinking about how to communicate it when you made the twitter call for questions.

Really simple example of measurable micro conversions on which I did put some efforts in, as simple as it gets: clicks on features like: related featured contents (which format/quantity/whatever drive more clicks and lower exit rate?), external links to other of our sites (its $$ baby! we have more then 200 websites), content sharing and so on.. (I will not start to talk about very specific ones!)

Even if you got afraid of not understanding, the response exceeded all my expectations. It's really not a won game yet, cause I'll need to access some data that is not that easily accessible (please don't ask me to sleep with anyone! :) to provide the right metrics to make someone take action – no one can support to keep losing money, huh?

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The Justin and Joe Teixeira questions are kind of the most important on a community point of view and I am very delighted that you did not hesitate to talk about them on a very clear positioning. Thanks.

The first one, from David Walizer, is that kind of question we really need to work like 1.000 days to get used to answer accordingly to our clients (internal or external) needing. Loved the way you approached.

On a strategic business standpoint, I liked very much the question from a great passionate market guy: Alex Cohen. I very much agree that multiplicity is the key, but let me admit that I would love to see some post by you around this topic. (maybe I am missing some of your older posts!) I am very sure that many ppl on our industry face difficult (at least on the start) due to very specific businesses needing that tools would not cover that easy.

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I truly believe this blog plays a definitive crucial role on the growing of Web Analyitcs Community. And I wish you could have access to a very specific KPI your blog could adopt: game changing on web analytics careers. Please, count me in.
Thank you.

I have been thinking about the existence of an X factor that goes beyond the measurable; I guess one of the ways to describe that is customer delight as you have.

I have noticed some web sites/platforms defy 'gravity', with lacking SEO, not the greatest content and even UI they still manage to deliver a sense of well-being or happiness that drives all else, ranking, interaction and ultimately conversion. I guess it's important to not caught up in the 'metrics' and remember that ultimately any software/website is ultimately human to human communication; something that is extremely hard to quantify or measure.

Great blog post; as mentioned above, a lot of information to absorb. *absorbing* :-)

Very interesting take from on the BI front. This kind of lies in the same line of thought where I have been talking for a while now within our org. We feel that both Retail and Marketing businesses are the biggest customer and the ones who would benefit the most from Web Analytics. WA is very different from the traditional BI. Although I strongly feel that the power of technology and the business centric focus is the key for the success of WA in an organization.

For starters, I will repeat what everyone has said; Thank you for answering my question. I have spent time reflecting your answer and I will have to say that I am slightly disappointed in the response. I was hoping that the answer will be more centric around actual insight/reporting with the data we gather from actual web analytic data (SC, GA, Webtrends, etc) and not actual user interaction insight; but that is one of the limitations of using twitter to post the question.

But like all web analytical data, we have to deal with what we are given and not what is expected (I meant one thing by the question, but it read differently). Now I completely agree with your analysis and agree that very few companies are actually using a form of survey to reflect what the voice of the customer is. I have actually been with a company before that had the ability to leave a comment on their website for over a year (by the time I joined) and I realized that no one was responsible for following up on any of the comments. An entire year of customer insight gone to waste. While I agree with the use of these surveys, I do believe this approach is not useful for everyone (a small start up company that may get 100 – 200 visits a day). Like all things, I believe this should only be used if the volume is there.

Avinash, thank you for taking the time to answer this question. while I agree with your response to the fact that this analysis is rarely done, I believe that there are other responses that I would have saved this wish for (ex, Fully understanding the requirements from the stakeholder, understanding & ensuring the data being reported is accurate are the first two that came to mind). But as this is what your opinion, I respect and honour it greatly as you are the reason I became a web analyst.

Keep up the good work Avinash, your posts always bring a smile to my face (especially the Web Analytics TV [the newer episodes show how much fun and love you have for your job]).

I really love this post so much! It's my favorite in a long time because you're just simply being prescriptive and brilliant about some really common (and not so common) issues in digital analytics. In my first comment in years (long time lurker you know that), I thought I create super paragraph where I contribute to the conversation rate by adding the largest word perhaps in the history of the Avinash Razor. Hope you can follow.

The BI answer is genial and fun – and you know what I did with it! :-) Being able to reference your answer about "clicks" is nice because we all know clicks will never equal visits no matter how vehemently people not as experienced in the dark arts of web data argue they should equal. You did forget the gentlemen over at Quantcast in your Competitive Intelligence answer – they are doing innovative work, and I think stopped doing sketchy things with LSO's (or if not shame, shame). Churn is really a SAS issue that I guess GA API could feed, but doing that type of work in GA isn't the best option. Available demand data for things like impressions can be found in tools like comScore AdMetrix. Nice callout on David McCandeless. He's amazing and so are his books, blog, and TED preso. Regarding qual/quant integration, it's partly another primary key issue – and honestly even the vendors who sell the survey tools don't have the best answers on how to do it (in my judgment). I hear what you are saying about page views, but they are useful in media and advertising conversations, for helping people form basic understanding of basic WA constructs, and can be helpful (sort of) when trying to predict available impressions or ad revenue (as just one of many, many data points to consider). Truly depressing about competitive conversions, but in certain industries there are data providers who can provide this data (though the data can "suck" big time and is as wrong as other digi data). Balancing conversion – that's right, regression but what type of regression is key? Logistic, quadratic, simple linear, supercalifragilisticexpialidocious… I think our pal Alex Cohen's question is really a hidden exclamation! :-) Certain groups have major resistance to being "sold" on optimization, so sometimes you sell up and they will tell down. Regarding non-profits and g-ment analytics, I think the gentlemen at Public Insite are doing great work in that area and overcoming resistance. My one wish is that we could deliver and prove we create profitable revenue (that there "economic value!"). There is no such thing as "easy" understanding of incrementality (imho). I know one company that has mixed web and customer data together to win ;-). The whole "web data is evil and destroys my privacy" is a lot of FUD but opt-in/out, disclosure, privacy policies in understandable language are all necessary to "check in" on that debate. lol. Another answer to the best way to create a multi-touch attribution model is "it depends" or less of cop-out "statistically." What did Tufte say about correlation and causation? Something about it's not, but it's a "good hint." One's own logic and data are inseparable – divorce them and be a broken business. I predict predictive analytics will continue to predict. :-) Social media data? The more things change, the more they stay the same and change. Big time industry? $1.8B to Adobe? GA Enterprise? IBM? ;-) No such things as the "best" analytics tool. They are just tools – it's like asking what's the best screwdriver (how big are your hands? what type of screw?). "Customer delight" = "satisfying customers" = "customer satisfaction." Perfect dashboard, no such thing. Even Earth Mother Nature Gaia is not perfect. Consider Palin or the Platypus. Kidding. :-)

Sarah: In terms of education three things work for me (mostly, but not always):

1. Educate everyone about what data is collected offline (say credit cards, cameras, etc). It is great context. :)

2. If the person is not your dad (or my parents)… distinguish website analytics from ad analytics and re-targeting. It is not that third party cookies are worse or better, its that they work differently. So when our parents anonymous data is collected for site analytics it is different (type and use).

3. Point out the strong opt out plugins, you can access the with one click on my privacy page.

The combination works, but it is never an easy non-painful discussion!

Timur: You make a great point. There are limits to what we can measure. From a business perspective we can measure if we make people happy by measuring Visitor Loyalty or Repeat Visitor Rate or Customer Satisfaction.

But each has its limitation. It is hard to say if all those metrics really measure true delight and happiness.

I'll give you a personal example. A reporter recently asked this this question: "What's the goal of your blog?" After thinking about it for a few seconds, only partly cheekily, I replied "To inspire irrational loyalty, at scale." That truly is my goal, but how does one measure it?

I have proxy metrics. But I also triangulate that with what people write about this blog, what cluster of happy and sad words they use, the emails I get every week from people who read it, and more things like that. At then end of that triangulation I have a sense for "irrational loyalty". In our business life for our website visitors we have to do the same, be that creative (and move outside Google Analytics or Omniture for measurement!).

John (Webbingyourway): I am so appreciative of the additional context in your comment. Thank you.

One of the things my life at Intuit (and training in Six Sigma / Process Excellence) taught me was to focus on root cause. The singular reason we as web analytics suck is not incomplete tags (this is a easily fixable problem), it is not that we don't understanding reporting requirements better (if you don't do this basic thing right you'll be out of a job and the next person can fix this), it is not that we don't have enough data from Site Catalyst or GA (both massive data puking machines), and it is not that Unique Visitors are not "accurate" (they are good enough, let's move on).

My humble experience has lead me to believe that reliance on Site Catalyst or WebTrends or CoreMetrics is a kiss of death. The ultimate massive obsession with constant implementation and reporting squirrel work. The starting point of: What are customer here and How have we failed them today, is a fantastic way to change the discussion. Not focus on tools or internal stake holders but the people we want to do business with.

It gets us to understand what's important, gives us a great starting point for our clickstream analysis, and in the long run reduces our focus on time wasting efforts of 90% of "web analysts" out there.

I will admit that I am absolutely biased against a technical focus on web analytics. I am deeply focused on business outcomes. I instinctively choose the strategic over the tactical (and frequently step over the disturbing typical WA time sinks). These biases influence my answers.

Thanks again for making me think harder about all this stuff.

Judah: Loved this stream of consciousness! I wanted to format it into little pieces thinking it would make it easier for people to follow, but that would mess with its awesomeness. :)

I think the questions by itself make this a good post as it allows folks to expand their "thinking horizons". Coupled with your answers it becomes a great post.

Some perspectives on a couple of areas:

Multi-channel marketing/Mixing web data with other customer data: You are absolutely right about the importance of primary key. Extending that thought a bit further, I would say that anybody thinking about doing multichannel analytics should spend quite a bit of time upfront understanding the current context, what is available and what is needed, process and infrastructure gaps & requirements etc. This might 'delay' things a bit but in the long run is absolutely worth it. (To get really useful insights, we really don't need to do a web data dump into the Customer Data warehouse and scoping things out can also result in potential cost savings).

Quantitative-Qualitative merge/Predictive analytics: One of the important facts that bubble up from your responses and based on my own experience is that a basic to intermediate knowledge of statistics can go a long way in making one an analytics Ninja. In addition, a knowledge of statistics can also help in addressing other issues one might face – sparse data, skewness, missing data etc. Combining or aggregating data is always easy. Understanding your data properties first and then applying the right methodology is really where the rubber meets the road when it comes to advance analytics.

BI: To me BI is nothing more than using the right tools and methodologies to gain insights into your business and customers. And no amount of human intelligence (not even a 10th degree analytics ninja) can whip up insights from thin air – you got to have data. The right data, at the right unit of analysis, and the right format. So my advice would be for folks to first spend some time ensuring they have good data and the required primary keys across their various digital/offline initiatives than worrying about which BI tool to use. (this is similar to the fallacy some folks have that using neural networks would give them better results by default)

Question #1 really struck a cord with me because it's something I've encountered a lot as a web agency (and not just with clients either – some other agencies just don't 'get' it either) and your answer was dead on as usual Avinash! It's all about giving a clear example of what utilising analytics will gain the customer.

Now we don't do anything nearly as complex or sophisticated as you do Avinash but from my limited experience I have found that talking about the benefits of analytics in terms of conversation rates etc doesn't always work but flat out saying that improving one quantity by X will increase revenue by Y is very powerful… and very persuasive. It's clear cut and directly links to tangible results.

Also sometimes having good case studies up your sleeve from previous clients can be really useful as well, especially if you don't have enough data on your new or potential client.

Benjamin: I had replied to my personal perspective on tag management solutions in a recent comment. Please check that out.

In a nutshell… As analytics solutions become ever more complex tag management solutions can be a real boon to those with massive web analytics implementations. Just look at the feature set of Tagman as an example. Pretty.

But a TMS's is not the end of the story, you'll still find some advanced implementations require you to touch the pages / site / rich content. So be aware of that.

Current Javascript tag and data capture functionality in most tools is 10 years old and has never been revisited. For the sake of humanity, while TMS's are awesome, I pray that web analytics vendors will optimize their tags, remove the gunk and move to technologies such as Async. Users, Implementators, Vendors,the Internet everyone will benefit.

Shakeel: Here is the simplest explanation. Analytics, traditional analytics, is considered to be a quantitative discipline because for the most part tools like Google Analytics and WebTrends etc only provide the ability to analyze clickstream data. Most of that data is "numbers".

In Web Analytics 2.0 we expand to use more sources and types of data. One of which is the inclusion of usability techniques like Lab Usability Testing, Heuristic Evaluations, Surveys which primarily collect Qualitative data. There are still numbers but the emphasis is on the qualitative experience of the person. We tend to call it qualitative analysis.

Greg: These are all fine tools. If you are looking for tools check out SiSense Prism as well.

The context of our discussion is less about the capabilities of the existing or new BI tool solutions or vendors. Some of these new guys do rethink some, or all, parts of the BI "stack" that are inefficient today. The big oldies in the space from Oracle to Informatica to SAS or SAP should be scared.

In this discussion my concern is more about how the BI function is organized, who staffs it, what exceptions of these folks are and the balance between reporting and analysis, and who / what / drives change, at what speed and by whom.

That is the part of the BI world that has gotten corrosive. The solutions you mention won't address most of those issues. I am not sure that the WA world has addressed all of them, but it most certainly is owned, operated and drives change differently and more expansively.

I run the Johnson & Johnson health channel on you tube. It is a non-branded channel, that is, not product-centric, though it provides health information about disease states in which we have businesses.

I've recorded a high number of views and comments. Being non "commercial", is there a way of measuring a quantative value to the company? You Tube provides a lot of metrics, but as far as I know you can't overlay things like Google Analytics.

Rob: What is it that you wanted to track in terms of value? That's sort of the key thing to figure out here.

If you are using YouTube Insights then you are getting an amazing amount of data already related to community engagement, demographics, views, audience attention etc. From that perspective your success is the ability to build an engaged community (subscribers, friend etc) and spread the JnJ brand message to many people and countries (just like your TV ads people are measuring it).

If you want to track the impact of your videos on links you have to JnJ.com or other sites you link to, then you can simply add campaign parameters to those links and then use Google Analytics, or an alternative tool, to track the impact of the people you are sending to those sites. In this case you can, as you put it, "overlay youtube activity impact on your site analytics data".

Finally if you get a brand channel from Google then you can work with your Account Rep to get even cooler and more directly connected data between YT and GA, see this post:

In the search reports you are seeing people who search on Google.com (or .uk or .hk etc etc).

In referral reports you are seeing people who come to your site from other Google properties. For example I have might a link to my blog from this website: http://www.google.com/analytics or a link at http://groups.google.com . Both of these are not search, yet are from the Google.com domain.

Those are the links you are seeing in referral analytics. Just drill down (right in Google Analytics) and it will show you the sites linking to you.

Slightly off topic, but as a fan of the site ( admittedly I have been away for a while) I thought I would pose this question.

I work for a software company and have a long running argument with my web team.

How can I convince them that the website is the best place for them to test different price points for our products?

I keep getting push back that the process is too hard too implement, as a non techie I can't argue with this. The other excuse I hear is that the traffic may not be sufficient for significant results or too much revenue is at risk.

Is an aternative route using PPC ads if changing the website proves too difficult?

Specifically #1 lack of budget / resources and #8 dealing with IT blockages.

In some sense you have to figure out how to compute the value of them saying yes or no. If your management team is willing to give you clear guidance about priorities then it becomes easier to figure out what the economic value of your work is and lots of barriers disappear.

Our website is quite clumsy that even if we presented different prices via specific individual product landing pages, if the customer added the product to basket then later abandoned back to the home page they would go back to seeing the 'standard prices'of the website.

This could be delicate if we are testing higher price points than current MSRP (i.e we show customer a $49.99 price point when MSRP is currently $44.99).

My client has 48 Google Analytics profiles/UA codes for 48 different domains. Several domains may belong to a single business unit and domain performance is tracked via the UA profile eg UA-1,UA-2,UA-3 = Biz Unit 1, additionally ecommerce transactions occur on some of these domains.

How can I ensure accurate GA reporting at a single domain as well as business unit level ? Additionally would you recommend a tag management solution ?

George: There's no standard answer that works for everyone in this type of a scenario. The best option is to hire a GACP to go through the requirements and validate and recommend the right path. You'll find a list here: http://www.bit.ly/gaac